Hanqi Jiang (蒋瀚祺)

University of Georgia Ph.D. Student, The University of Georgia (2024)

I am a second-year PhD student at University of Georgia, supervised by Distinguished Research Professor Tianming Liu. My research focus is Quantum AI & Medical Image Analysis & Brain-inspired AI. If you are seeking any form of academic cooperation, please feel free to email me at hanqi.jiang@uga.edu (UGA) or hjiang81@mgh.harvard.edu (MGH).

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Education
  • University of Georgia

    University of Georgia

    Ph.D. in Computer Science Sep. 2024 - Now

  • Lancaster University

    Lancaster University

    B.S. in Computer Science (First-Class Honored) Sep. 2019 - Jul. 2023

  • Beijing Jiaotong University

    Beijing Jiaotong University

    B.Eng. in Computer Science Sep. 2019 - Jul. 2023

Honors & Awards
  • NSF Student Travel Award, AAAI FSS25 (QIML) 2025
  • Outstanding Graduation Project of Beijing Province 2023
Experience
  • School of Computing, UGA

    School of Computing, UGA

    Research Assistant & Teaching Assistant Sep. 2024 - Now

  • Center for Advanced Medical Computing and Analysis (CAMCA), MGB/Harvard Medical School

    Center for Advanced Medical Computing and Analysis (CAMCA), MGB/Harvard Medical School

    Radiology Research Mar. 2025 - Now

  • School of Data Science, CUHK(SZ)

    School of Data Science, CUHK(SZ)

    Research Assistant Jul. 2023 - Sep. 2023

Service
News
2025
🎉 A paper is accepted by QIML AAAI Fall 2025 Symposium!
Aug 25
🎉 A paper is accepted by IEEE Transactions on Biomedical Engineering (IF=4.5)!
Aug 13
🎉 A paper is accepted by 2025 IEEE International Conference on Quantum Artificial Intelligence!
Aug 10
🎉 A paper is accepted by IEEE Transactions on Neural Networks and Learning Systems (IF=10.2)!
Jun 13
🎓 I transferred to the University of Georgia, Franklin College of Arts and Sciences, School of Computing as a PhD student of Computer Science.
Apr 16
👨‍🔬 I joined Harvard Medical School and Massachusetts General Hospital as an intern, guided by Prof. Xiang Li.
Mar 19
🎉 A paper is accepted by ICLR 2025!
Jan 21
🎉 A paper is accepted by ISBI 2025 (oral presentation), see you in Houston!
Jan 01
2024
🎉 A paper is accepted by AAAI 2025!
Dec 08
🎉 A paper is accepted by IEEE Reviews in Biomedical Engineering (IF=17.2)!
Oct 30
🎉 A paper is accepted by NeurIPS 2024, see you in Vancouver!
Sep 23
🎉 A paper is accepted by ECCV 2024 Workshop!
Aug 18
🎉 A paper is accepted by ICIC 2024 (oral presentation)!
May 30
🎉 A paper is accepted by Automotive Innovation (IF=6.1)!
Apr 08
2023
🎓 I will join the University of Georgia, College of Engineering as a PhD student, guided by Prof. Tianming Liu.
Oct 10
👨‍🔬 I joined the Chinese University of Hong Kong (Shenzhen), School of Data Science as a research assistant, guided by Prof. Ruimao Zhang.
Jul 01
2022
👨‍🔬 I joined the State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences (ICTCAS) as an intern, guided by Prof. Yinhe Han.
Apr 24
Selected Publications (view all )
Bridging Classical and Quantum Computing for Next-Generation Language Models
Bridging Classical and Quantum Computing for Next-Generation Language Models

Yi Pan*, Hanqi Jiang*, Junhao Chen, Yiwei Li, Huaqin Zhao, Lin Zhao, Yohannes Abate, Yingfeng Wang†, Tianming Liu†(* equal contribution)(† corresponding author)

AAAI QIML 2025 Conference

We introduce Adaptive Quantum-Classical Fusion (AQCF), the first framework to bridge quantum and classical computing through dynamic, quantum-classical co-design for next-generation language models. We introduce Adaptive Quantum-Classical Fusion (AQCF), the first framework to bridge quantum and classical computing through dynamic, quantum-classical co-design for next-generation language models.

[Paper]

Bridging Classical and Quantum Computing for Next-Generation Language Models
Bridging Classical and Quantum Computing for Next-Generation Language Models

Yi Pan*, Hanqi Jiang*, Junhao Chen, Yiwei Li, Huaqin Zhao, Lin Zhao, Yohannes Abate, Yingfeng Wang†, Tianming Liu†(* equal contribution)(† corresponding author)

AAAI QIML 2025 Conference

We introduce Adaptive Quantum-Classical Fusion (AQCF), the first framework to bridge quantum and classical computing through dynamic, quantum-classical co-design for next-generation language models. We introduce Adaptive Quantum-Classical Fusion (AQCF), the first framework to bridge quantum and classical computing through dynamic, quantum-classical co-design for next-generation language models.

[Paper]

MolQAE: Quantum Autoencoder for Molecular Representation Learning
MolQAE: Quantum Autoencoder for Molecular Representation Learning

Yi Pan*, Hanqi Jiang*, Wei Ruan, Dajiang Zhu, Xiang Li, Yohannes Abate, Yingfeng Wang†, Tianming Liu†(* equal contribution)(† corresponding author)

QAI 2025 Conference

Quantum Autoencoder for Molecular Representation Learning. Quantum Autoencoder for Molecular Representation Learning.

[Paper]

MolQAE: Quantum Autoencoder for Molecular Representation Learning
MolQAE: Quantum Autoencoder for Molecular Representation Learning

Yi Pan*, Hanqi Jiang*, Wei Ruan, Dajiang Zhu, Xiang Li, Yohannes Abate, Yingfeng Wang†, Tianming Liu†(* equal contribution)(† corresponding author)

QAI 2025 Conference

Quantum Autoencoder for Molecular Representation Learning. Quantum Autoencoder for Molecular Representation Learning.

[Paper]

Argus: Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models
Argus: Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models

Yifan Xu, Chao Zhang, Hanqi Jiang, Xiaoyan Wang, Ruifei Ma, Yiwei Li, Zihao Wu, Zeju Li, Xiangde Liu†(† corresponding author)

IEEE Transactions on Neural Networks and Learning Systems 2025 Journal (IF=10.2)

Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models. Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models.

[Paper]

Argus: Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models
Argus: Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models

Yifan Xu, Chao Zhang, Hanqi Jiang, Xiaoyan Wang, Ruifei Ma, Yiwei Li, Zihao Wu, Zeju Li, Xiangde Liu†(† corresponding author)

IEEE Transactions on Neural Networks and Learning Systems 2025 Journal (IF=10.2)

Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models. Leveraging Multi-View Images for Improved 3D Scene Understanding with Large Language Models.

[Paper]

ECHOPulse: ECG Controlled Echocardiograms Video Generation
ECHOPulse: ECG Controlled Echocardiograms Video Generation

Yiwei Li, Sekeun Kim, Zihao Wu, Hanqi Jiang, Yi Pan, Pengfei Jin, Sifan Song, Yucheng Shi, Xiaowei Yu, Tianze Yang, Tianming Liu†, Quanzheng Li†, Xiang Li†(† corresponding author)

ICLR 2025 Conference

We propose ECHOPluse, an ECG-conditioned ECHO video generation model. ECHOPluse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token modeling for fast decoding, and (2) it conditions on readily accessible ECG… We propose ECHOPluse, an ECG-conditioned ECHO video generation model. ECHOPluse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token modeling for fast decoding, and (2) it conditions on readily accessible ECG signals, which are highly coherent with ECHO videos, bypassing complex conditional prompts. To the best of our knowledge, this is the first work to use time-series prompts like ECG signals for ECHO video generation. ECHOPluse not only enables controllable synthetic ECHO data generation but also provides updated cardiac function information for disease monitoring and prediction beyond ECG alone. Evaluations on three public and private datasets demonstrate state-of-the-art performance in ECHO video generation across both qualitative and quantitative measures.

[Paper] [Demo]

ECHOPulse: ECG Controlled Echocardiograms Video Generation
ECHOPulse: ECG Controlled Echocardiograms Video Generation

Yiwei Li, Sekeun Kim, Zihao Wu, Hanqi Jiang, Yi Pan, Pengfei Jin, Sifan Song, Yucheng Shi, Xiaowei Yu, Tianze Yang, Tianming Liu†, Quanzheng Li†, Xiang Li†(† corresponding author)

ICLR 2025 Conference

We propose ECHOPluse, an ECG-conditioned ECHO video generation model. ECHOPluse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token modeling for fast decoding, and (2) it conditions on readily accessible ECG… We propose ECHOPluse, an ECG-conditioned ECHO video generation model. ECHOPluse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token modeling for fast decoding, and (2) it conditions on readily accessible ECG signals, which are highly coherent with ECHO videos, bypassing complex conditional prompts. To the best of our knowledge, this is the first work to use time-series prompts like ECG signals for ECHO video generation. ECHOPluse not only enables controllable synthetic ECHO data generation but also provides updated cardiac function information for disease monitoring and prediction beyond ECG alone. Evaluations on three public and private datasets demonstrate state-of-the-art performance in ECHO video generation across both qualitative and quantitative measures.

[Paper] [Demo]

EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis

Yi Pan*, Hanqi Jiang*, Junhao Chen, Yiwei Li, Huaqin Zhao, Yifan Zhou, Peng Shu, Zihao Wu, Zhengliang Liu, Dajiang Zhu, Xiang Li, Yohannes Abate, Tianming Liu†(* equal contribution)(† corresponding author)

ISBI 2025 Oral Conference

Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis. Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis.

[Paper]

EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis

Yi Pan*, Hanqi Jiang*, Junhao Chen, Yiwei Li, Huaqin Zhao, Yifan Zhou, Peng Shu, Zihao Wu, Zhengliang Liu, Dajiang Zhu, Xiang Li, Yohannes Abate, Tianming Liu†(* equal contribution)(† corresponding author)

ISBI 2025 Oral Conference

Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis. Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis.

[Paper]

Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning
Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning

Chong Ma, Hanqi Jiang, Wenting Chen, Yiwei Li, Zihao Wu, Xiaowei Yu, Zhengliang Liu, Lei Guo, Dajiang Zhu, Tuo Zhang, Dinggang Shen, Tianming Liu†, Xiang Li†(† corresponding author)

NeurIPS 2024 Conference

We propose EGMA, a novel framework for medical multi-modal alignment, marking the first attempt to integrate eye-gaze data into vision-language pre-training. EGMA outperforms existing state-of-the-art medical multi-modal pre-training methods, and realizes notable enhancements in image classification and image-text retrieval tasks.… We propose EGMA, a novel framework for medical multi-modal alignment, marking the first attempt to integrate eye-gaze data into vision-language pre-training. EGMA outperforms existing state-of-the-art medical multi-modal pre-training methods, and realizes notable enhancements in image classification and image-text retrieval tasks. EGMA demonstrates that even a small amount of eye-gaze data can effectively assist in multi-modal pre-training and improve the feature representation ability of the model.

[Paper]

Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning
Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning

Chong Ma, Hanqi Jiang, Wenting Chen, Yiwei Li, Zihao Wu, Xiaowei Yu, Zhengliang Liu, Lei Guo, Dajiang Zhu, Tuo Zhang, Dinggang Shen, Tianming Liu†, Xiang Li†(† corresponding author)

NeurIPS 2024 Conference

We propose EGMA, a novel framework for medical multi-modal alignment, marking the first attempt to integrate eye-gaze data into vision-language pre-training. EGMA outperforms existing state-of-the-art medical multi-modal pre-training methods, and realizes notable enhancements in image classification and image-text retrieval tasks.… We propose EGMA, a novel framework for medical multi-modal alignment, marking the first attempt to integrate eye-gaze data into vision-language pre-training. EGMA outperforms existing state-of-the-art medical multi-modal pre-training methods, and realizes notable enhancements in image classification and image-text retrieval tasks. EGMA demonstrates that even a small amount of eye-gaze data can effectively assist in multi-modal pre-training and improve the feature representation ability of the model.

[Paper]

Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity
Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity

Hanqi Jiang, Xixuan Hao, Yuzhou Huang, Chong Ma, Jiaxun Zhang, Yi Pan, Ruimao Zhang†(† corresponding author)

ECCV Workshop 2024 Conference

We present a medical vision-language pre-training (Med-VLP) framework that incorporates multi-modal contrastive alignment and parallel generative streams with multi-level semantic hierarchies. To accomplish this goal, we effectively leverage the characteristics of medical data. By optimizing elaborate training objectives, our HybridMED… We present a medical vision-language pre-training (Med-VLP) framework that incorporates multi-modal contrastive alignment and parallel generative streams with multi-level semantic hierarchies. To accomplish this goal, we effectively leverage the characteristics of medical data. By optimizing elaborate training objectives, our HybridMED is capable of efficiently executing a variety of downstream tasks, including cross-modal, uni-modal, and multi-modal types. Extensive experimental results demonstrate that our HybridMED can deliver highly satisfactory performance across a wide array of downstream tasks, thereby validating the model's superiority.

[Paper]

Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity
Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity

Hanqi Jiang, Xixuan Hao, Yuzhou Huang, Chong Ma, Jiaxun Zhang, Yi Pan, Ruimao Zhang†(† corresponding author)

ECCV Workshop 2024 Conference

We present a medical vision-language pre-training (Med-VLP) framework that incorporates multi-modal contrastive alignment and parallel generative streams with multi-level semantic hierarchies. To accomplish this goal, we effectively leverage the characteristics of medical data. By optimizing elaborate training objectives, our HybridMED… We present a medical vision-language pre-training (Med-VLP) framework that incorporates multi-modal contrastive alignment and parallel generative streams with multi-level semantic hierarchies. To accomplish this goal, we effectively leverage the characteristics of medical data. By optimizing elaborate training objectives, our HybridMED is capable of efficiently executing a variety of downstream tasks, including cross-modal, uni-modal, and multi-modal types. Extensive experimental results demonstrate that our HybridMED can deliver highly satisfactory performance across a wide array of downstream tasks, thereby validating the model's superiority.

[Paper]

All publications